ML Power Generation Control With Invalid Data Correction
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Solution Overview
Problem
Complex thermodynamic power generation systems, such as those using turbogenerators, face challenges in optimizing steam usage and power generation due to the complexity of controlling multiple components and the variability of data quality, leading to inefficiencies and increased costs.
Innovation Solution
The implementation of machine learning-based optimization methods that analyze data to classify and correct invalid information, train models to predict power generation, and generate control instructions to optimize steam flows and component operations within physical and engineering constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to optimize thermodynamic power generation, then power production efficiency is improved, but data quality issues and system complexity increase
Solution Approach 1:
The system segments the complex optimization problem into distinct functional modules: data validation module, machine learning model module, and control instruction generation module. Each module handles specific tasks independently, making the overall system more manageable and maintainable while achieving high power production efficiency through coordinated operation of these segmented components.
Solution Approach 2:
The patent introduces an intermediary data validation layer between the raw data input and the machine learning model. This intermediary validates and cleans data before processing, preventing quality issues from propagating through the system. Additionally, control instruction intermediaries translate model predictions into actionable commands for the thermodynamic system, managing the complexity of system-control integration.
2Measurement precision
If data validation and correction processes are implemented, then model accuracy is improved, but processing time increases
Solution Approach 1:
The system performs data validation and correction as preliminary actions before feeding data to the machine learning model. By validating data upfront and correcting issues early in the processing pipeline, the system ensures high model accuracy without requiring repeated processing or corrections later, thus minimizing overall processing time.
Solution Approach 2:
The patent replaces manual or rule-based data correction methods with automated machine learning-based correction models. These intelligent systems quickly identify and correct data anomalies, invalidations, and inconsistencies much faster than traditional mechanical or manual validation processes, maintaining high accuracy while reducing processing time.
3Reliability
If multiple machine learning models are trained and deployed, then optimization recommendations are improved, but computational resources and training time increase
Solution Approach 1:
The system segments the optimization task across multiple specialized machine learning models, where each model is trained to handle specific aspects of thermodynamic power generation optimization. This segmentation allows for more focused and efficient training of individual models compared to a single monolithic model, improving overall recommendation quality while managing computational resources through distributed processing.
Solution Approach 2:
The patent employs parameter changes and hyperparameter tuning to optimize the performance of multiple machine learning models. By carefully adjusting training parameters, model architectures, and computational settings, the system achieves high reliability in optimization recommendations while minimizing the computational energy required for training and deployment.
Data Source
AI summary
A method includes analyzing information to be processed, where analyzing the information includes classifying invalid data contained in the information and substituting replacement data in place of at least some of the invalid data in the information. The method also includes training at least one machine learning model based on some of the analyzed information. The method further includes providing other of the analyzed information to the at least one trained machine learning model, where the at least one trained machine learning model is used to generate one or more recommendations based on the analyzed information. In addition, the method includes translating each of the one or more recommendations into one or more actions and generating one or more control instructions based on the one or more actions for at least one of the one or more recommendations.